SemiEvol

This repository contains the official implementation of the paper: SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation.

TLDR

We have developed SemiEvol, which leverages labeled data to enhance inference on unlabeled data, thereby improving the reasoning capabilities of Large Language Models (LLMs). This process is characterized as a bi-level knowledge propagation and selection mechanism.

Framework

SemiEvol maximizes the utility of labeled data through a bi-level knowledge propagation-and-selection framework, while leveraging collaborative learning among multiple LLMs to exploit unlabeled data, unleashing the full data potential.

Installation

Install dependencies using the requirements.txt file:

pip install -r requirements.txt

Dataset and Released Model Weights

• Dataset: https://huggingface.co/datasets/luojunyu/SemiEvol

We have released model weights for two tasks to facilitate testing:

• SemiEvol on MMLU based on Llama-3.1-8B: https://huggingface.co/luojunyu/Llama-3.1-8B-SemiEvol-MMLU
• SemiEvol on MMLU-Pro based on Llama-3.1-8B: https://huggingface.co/luojunyu/Llama-3.1-8B-SemiEvol-MMLUPro

Usage

• To run the SemiEvol process, use:

python pipeline.py --task mmlu --model llama3.1

You can adjust parameters such as task, model, and other hyperparameters in pipeline.py.

Please modify the parameters in the code before execution.

Note that you need to register your model in config.py.

BibTex

If our work has been helpful to you, please consider citing it. Your citation serves as encouragement for our research.

@misc{luo2024semievol,
    title={SemiEvol: Semi-supervised Fine-tuning for LLM Adaptation}, 
    author={Junyu Luo and Xiao Luo and Xiusi Chen and Zhiping Xiao and Wei Ju and Ming Zhang},
    year={2024},
    eprint={2410.14745},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2410.14745}, 
}

You can also find the paper at HuggingFace.